Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor

• URL: http://arxiv.org/abs/2306.03951v2
• Date: Wed, 14 Jun 2023 09:11:20 GMT
• Title: Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor
• Authors: Arshad Javeed, Valent\'in L\'opez Jim\'enez
• Abstract summary: The objective of the project is to explore synergies between classical control algorithms such as PID and contemporary reinforcement learning algorithms. The primary objective would be performing PID tuning using reinforcement learning strategies. The secondary objective is to leverage the learnings to implement control for navigation by integrating with the lighthouse positioning system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of the project is to explore synergies between classical control algorithms such as PID and contemporary reinforcement learning algorithms to come up with a pragmatic control mechanism to control the CrazyFlie 2.X quadrotor. The primary objective would be performing PID tuning using reinforcement learning strategies. The secondary objective is to leverage the learnings from the first task to implement control for navigation by integrating with the lighthouse positioning system. Two approaches are considered for navigation, a discrete navigation problem using Deep Q-Learning with finite predefined motion primitives, and deep reinforcement learning for a continuous navigation approach. Simulations for RL training will be performed on gym-pybullet-drones, an open-source gym-based environment for reinforcement learning, and the RL implementations are provided by stable-baselines3

Related papers

• Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning [5.229380825007323]
  We propose a reinforcement learning (RL)-based algorithm to jointly train (1) a trajectory planner and (2) a tracking controller in a layered control architecture. Our algorithm arises naturally from a rewrite of the underlying optimal control problem that lends itself to an actor-critic learning approach.
  arXiv  Detail & Related papers  (2024-08-03T02:53:24Z)
• NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning [101.56342075720588]
  Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. This paper introduces a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision.
  arXiv  Detail & Related papers  (2024-03-12T07:27:02Z)
• Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF [82.73541793388]
  We introduce the first principled algorithmic framework for solving bilevel RL problems through the lens of penalty formulation. We provide theoretical studies of the problem landscape and its penalty-based gradient (policy) algorithms. We demonstrate the effectiveness of our algorithms via simulations in the Stackelberg Markov game, RL from human feedback and incentive design.
  arXiv  Detail & Related papers  (2024-02-10T04:54:15Z)
• Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning [27.73555826776087]
  Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data. In this paper, we pair a recent algorithm for automatically building curricula with RL on massively parallelized simulations. We demonstrate the potential of curriculum RL to jointly learn state estimation and control for non-linear tracking tasks.
  arXiv  Detail & Related papers  (2023-09-25T12:48:47Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [71.63186089279218]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• Katakomba: Tools and Benchmarks for Data-Driven NetHack [52.0035089982277]
  NetHack is known as the frontier of reinforcement learning research. We argue that there are three major obstacles for adoption: resource-wise, implementation-wise, and benchmark-wise. We develop an open-source library that provides workflow fundamentals familiar to the offline reinforcement learning community.
  arXiv  Detail & Related papers  (2023-06-14T22:50:25Z)
• CT-DQN: Control-Tutored Deep Reinforcement Learning [4.395396671038298]
  Control-Tutored Deep Q-Networks (CT-DQN) is a Deep Reinforcement Learning algorithm that leverages a control tutor to reduce learning time. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing.
  arXiv  Detail & Related papers  (2022-12-02T17:59:43Z)
• Collision-Free Flocking with a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning [2.555094847583209]
  We deal with the decentralized leader-follower flocking control problem through deep reinforcement learning (DRL) We propose a novel reinforcement learning algorithm CACER-II for training a shared control policy for all the followers. As a result, the variable-length system state can be encoded into a fixed-length embedding vector, which makes the learned DRL policies independent with the number or the order of followers.
  arXiv  Detail & Related papers  (2021-01-20T11:23:35Z)
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning [102.78958681141577]
  We present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy deep reinforcement learning algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration.
  arXiv  Detail & Related papers  (2020-07-09T17:08:44Z)
• Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments [11.657524999491029]
  In this work, we used deep reinforcement learning combining Q-learning with a neural representation to avoid instability. Our methodology uses deep q-learning and combines it with a rolling wave planning approach on agile methodology. Experimental results show that the proposed method enhanced the performance of VVN by 55.31 on average for long-distance missions.
  arXiv  Detail & Related papers  (2020-03-23T12:58:58Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.